A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems

This paper considers a transportation problem for moving empty or laden containers for a logistic company. Owing to the limited resource of its vehicles (trucks and trailers), the company often needs to sub-contract certain job orders to outsourced companies. A model for this truck and trailer vehic...

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Vydané v:European journal of operational research Ročník 172; číslo 3; s. 855 - 885
Hlavní autori: Tan, K.C., Chew, Y.H., Lee, L.H.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 01.08.2006
Elsevier
Elsevier Sequoia S.A
Edícia:European Journal of Operational Research
Predmet:
Algorithms
Applied sciences
Decision theory. Utility theory
Evolutionary algorithms
Exact sciences and technology
Flows in networks. Combinatorial problems
Genetic algorithms
Heuristic
Job orders
Logistics
Multi-objective optimization
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
Route optimization
Studies
Transportation problem (Operations research)
Trucks
Vehicle routing
Vehicles
Vehicle routing
Multi-objective optimization
Evolutionary algorithms
Local search
Pareto optimum
Decision support system
Decision making
Evolutionary algorithm
Vehicle routing problem
Multiobjective programming
Combinatorial optimization
Optimization
Container
Transportation problem
Time window
Minimal distance
Logistics
ISSN:0377-2217, 1872-6860
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Shrnutí:This paper considers a transportation problem for moving empty or laden containers for a logistic company. Owing to the limited resource of its vehicles (trucks and trailers), the company often needs to sub-contract certain job orders to outsourced companies. A model for this truck and trailer vehicle routing problem (TTVRP) is first constructed in the paper. The solution to the TTVRP consists of finding a complete routing schedule for serving the jobs with minimum routing distance and number of trucks, subject to a number of constraints such as time windows and availability of trailers. To solve such a multi-objective and multi-modal combinatorial optimization problem, a hybrid multi-objective evolutionary algorithm (HMOEA) featured with specialized genetic operators, variable-length representation and local search heuristic is applied to find the Pareto optimal routing solutions for the TTVRP. Detailed analysis is performed to extract useful decision-making information from the multi-objective optimization results as well as to examine the correlations among different variables, such as the number of trucks and trailers, the trailer exchange points, and the utilization of trucks in the routing solutions. It has been shown that the HMOEA is effective in solving multi-objective combinatorial optimization problems, such as finding useful trade-off solutions for the TTVRP routing problem.
Bibliografia:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2004.11.019